This research introduces a novel semantic safety framework that enables robots to understand and respect human-intuitive safety constraints during manipulation tasks.

• Integrates semantic scene understanding with robotic control systems to prevent unsafe actions like moving water over electronics
• Implements control barrier functions that dynamically adapt to changing environments and object relationships
• Demonstrates successful prevention of unsafe manipulations in real-world experiments without compromising task completion
• Provides a computationally efficient approach that can be deployed in real-time applications

For engineering teams, this technology significantly reduces safety risks in human-robot collaborative environments while maintaining operational efficiency, marking an important step toward more intelligent and trustworthy robotic systems.

Semantically Safe Robot Manipulation: From Semantic Scene Understanding to Motion Safeguards